{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/justas/anaconda2/envs/p27/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not load NEURONBackend\n"
     ]
    }
   ],
   "source": [
    "from neuronunit.tests.druckmann2013 import AP1AmplitudeTest, AP1DelayMeanStrongStimTest\n",
    "from neuronunit.models.neuron_cell import NeuronCellModel\n",
    "import os\n",
    "import requests\n",
    "import quantities as pq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_extract_compile_model(nmldb_modelid):\n",
    "    # Get info about the model from NMLDB API\n",
    "    model_info_url = \"https://neuroml-db.org/api/model?id=\"+nmldb_modelid\n",
    "    model_info = requests.get(model_info_url).json()\n",
    "\n",
    "    # Download model from NMLDB\n",
    "    os.system('wget -O model.zip \"https://neuroml-db.org/GetModelZip?modelID='+nmldb_modelid+'&version=NeuroML\"')\n",
    "\n",
    "    # Unzip the model to sub dir\n",
    "    os.system(\"unzip -o model.zip -d \" + nmldb_modelid)\n",
    "\n",
    "    # Change the CWD to the model dir\n",
    "    os.chdir(nmldb_modelid)\n",
    "\n",
    "    # Convert NML to NEURON using jNeuroML\n",
    "    cell_file_name = model_info[\"model\"][\"File_Name\"]\n",
    "\n",
    "    os.system(\"jnml \"+cell_file_name+\" -neuron -nogui\")\n",
    "\n",
    "    # Compile any .mod files\n",
    "    os.system(\"nrnivmodl\")\n",
    "    \n",
    "    return model_info\n",
    "\n",
    "\n",
    "def get_hoc_files():\n",
    "    return [f for f in os.listdir('.') if f.endswith(\".hoc\")]\n",
    "\n",
    "def get_mod_files():\n",
    "    return [f for f in os.listdir('.') if f.endswith(\".mod\")]\n",
    "    \n",
    "def is_abstract_cell():\n",
    "    return len(get_hoc_files()) == 0 and len(get_mod_files()) == 1\n",
    "\n",
    "def get_abstract_cell(h):\n",
    "    cell_mod_file = get_mod_files()[0]\n",
    "    cell_mod_name = cell_mod_file.replace(\".mod\", \"\")\n",
    "\n",
    "    soma = h.Section()\n",
    "    soma.L = 10\n",
    "    soma.diam = 10\n",
    "    soma.cm = 318.31927  # Magic number, see: https://github.com/NeuroML/org.neuroml.export/issues/60\n",
    "\n",
    "    mod = getattr(h, cell_mod_name)(0.5, sec=soma)\n",
    "\n",
    "    return soma, mod\n",
    "\n",
    "def get_cell_with_morphology(h):\n",
    "    cell_hoc_file = get_hoc_files()[0]\n",
    "    cell_template = cell_hoc_file.replace(\".hoc\", \"\")\n",
    "    h.load_file(cell_hoc_file)\n",
    "    cell = getattr(h, cell_template)()\n",
    "    return cell\n",
    "\n",
    "def get_soma(h):\n",
    "    # Get the root sections and try to find the soma\n",
    "    roots = h.SectionList()\n",
    "    roots.allroots()\n",
    "    roots = [s for s in roots]\n",
    "    somas = [sec for sec in roots if \"soma\" in sec.name().lower()]\n",
    "    if len(somas) == 1:\n",
    "        return somas[0]\n",
    "    elif len(somas) == 0 and len(roots) == 1:\n",
    "        return roots[0]\n",
    "    else:\n",
    "        raise Exception(\"Problem finding the soma section\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 29 is mitral cell, 28 granule. For more see: https://neuroml-db.org/search_model?q=olfactory\n",
    "model_info = download_extract_compile_model(nmldb_modelid = 'NMLCL001128') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load model in NEURON\n",
    "from neuron import h,gui\n",
    "\n",
    "if is_abstract_cell():\n",
    "    cell, mod = get_abstract_cell(h)\n",
    "else:\n",
    "    cell = get_cell_with_morphology(h)\n",
    "\n",
    "soma = get_soma(h)\n",
    "\n",
    "h.cvode_active(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a NeuronUnit model from the NEURON model\n",
    "name = model_info[\"model\"][\"Name\"]\n",
    "temp = model_info[\"publication\"][\"record\"][\"Temperature\"]\n",
    "\n",
    "cell_model = NeuronCellModel(soma(0.5), name=name)\n",
    "cell_model.set_stop_time(3000*pq.ms)\n",
    "cell_model.set_temperature(temp)\n",
    "\n",
    "h.newPlotV() # optional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the Druckmann2013 \"standard\" and \"strong\" currents\n",
    "rheobase = model_info[\"model\"][\"Rheobase_High\"] * pq.nA\n",
    "\n",
    "standard = rheobase * 1.5\n",
    "strong = rheobase * 3.0\n",
    "\n",
    "temp = model_info[\"publication\"][\"record\"][\"Temperature\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t-55.325344 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean': array([50.6126289]) * mV, 'n': 1, 'std': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Druckmann standard current test\n",
    "test = AP1AmplitudeTest(current_amplitude=standard, observation={\"mean\":30*pq.mV,\"std\":5*pq.mV, \"n\":1})\n",
    "test.generate_prediction(cell_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean': array(8.) * ms, 'n': 1, 'std': 0}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another test using the strong current\n",
    "test = AP1DelayMeanStrongStimTest(current_amplitude=strong, observation={\"mean\":100*pq.ms,\"std\":5*pq.ms, \"n\":1})\n",
    "test.params[\"repetitions\"] = 1\n",
    "\n",
    "test.generate_prediction(cell_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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